library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
library(psych)
library(whitening)
library("vioplot")
library("rpart")
library(mlbench)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Source W. Nick Street, Olvi L. Mangasarian and William H. Wolberg (1995). An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522–530, San Francisco, Morgan Kaufmann.
Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
wpbc {TH.data}
data("wpbc", package = "TH.data")
table(wpbc[,"status"])
#>
#> N R
#> 151 47
sum(1*(wpbc[,"status"]=="R" & wpbc$time <= 24))
#> [1] 29
wpbc <- subset(wpbc,time > 36 | status=="R" )
summary(wpbc$time)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 36.75 60.50 58.79 78.75 125.00
wpbc[,"status"] <- 1*(wpbc[,"status"]=="R")
wpbc <- wpbc[complete.cases(wpbc),]
pander::pander(table(wpbc[,"status"]))
| 0 | 1 |
|---|---|
| 91 | 46 |
wpbc$time <- NULL
studyName <- "Wisconsin"
dataframe <- wpbc
outcome <- "status"
thro <- 0.4
TopVariables <- 10
cexheat = 0.25
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 137 | 32 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 91 | 46 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9961379
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 32 , Uni p: 0.0233331 , Uncorrelated Base: 4 , Outcome-Driven Size: 0 , Base Size: 4
#>
#>
1 <R=0.996,r=0.973,N= 6>, Top: 2( 2 )[ 1 : 2 Fa= 2 : 0.973 ]( 2 , 4 , 0 ),<|>Tot Used: 6 , Added: 4 , Zero Std: 0 , Max Cor: 0.968
#>
2 <R=0.968,r=0.959,N= 6>, Top: 1( 1 )[ 1 : 1 Fa= 3 : 0.959 ]( 1 , 1 , 2 ),<|>Tot Used: 8 , Added: 1 , Zero Std: 0 , Max Cor: 0.911
#>
3 <R=0.911,r=0.905,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 3 : 0.905 ]( 2 , 2 , 3 ),<|>Tot Used: 9 , Added: 2 , Zero Std: 0 , Max Cor: 0.895
#>
4 <R=0.895,r=0.847,N= 11>, Top: 6( 1 )[ 1 : 6 Fa= 8 : 0.847 ]( 5 , 5 , 3 ),<|>Tot Used: 18 , Added: 5 , Zero Std: 0 , Max Cor: 0.842
#>
5 <R=0.842,r=0.821,N= 11>, Top: 3( 1 )[ 1 : 3 Fa= 8 : 0.821 ]( 3 , 3 , 8 ),<|>Tot Used: 20 , Added: 3 , Zero Std: 0 , Max Cor: 0.796
#>
6 <R=0.796,r=0.598,N= 11>, Top: 5( 3 )[ 1 : 5 Fa= 12 : 0.598 ]( 5 , 9 , 8 ),<|>Tot Used: 23 , Added: 9 , Zero Std: 0 , Max Cor: 0.785
#>
7 <R=0.785,r=0.592,N= 11>, Top: 4( 1 )[ 1 : 4 Fa= 14 : 0.592 ]( 4 , 6 , 12 ),<|>Tot Used: 26 , Added: 6 , Zero Std: 0 , Max Cor: 0.739
#>
8 <R=0.739,r=0.570,N= 11>, Top: 3( 2 )[ 1 : 3 Fa= 14 : 0.570 ]( 3 , 4 , 14 ),<|>Tot Used: 28 , Added: 4 , Zero Std: 0 , Max Cor: 0.560
#>
9 <R=0.560,r=0.480,N= 11>, Top: 9[ 2 ]( 1 )=[ 2 : 9 Fa= 16 : 0.531 ]( 8 , 11 , 14 ),<|>Tot Used: 32 , Added: 11 , Zero Std: 0 , Max Cor: 0.582
#>
10 <R=0.582,r=0.491,N= 11>, Top: 4( 1 )[ 1 : 4 Fa= 17 : 0.491 ]( 4 , 4 , 16 ),<|>Tot Used: 32 , Added: 4 , Zero Std: 0 , Max Cor: 0.765
#>
11 <R=0.765,r=0.583,N= 11>, Top: 2( 1 )[ 1 : 2 Fa= 18 : 0.583 ]( 2 , 2 , 17 ),<|>Tot Used: 32 , Added: 2 , Zero Std: 0 , Max Cor: 0.522
#>
12 <R=0.522,r=0.461,N= 8>, Top: 3( 3 )[ 1 : 3 Fa= 18 : 0.461 ]( 2 , 5 , 18 ),<|>Tot Used: 32 , Added: 5 , Zero Std: 0 , Max Cor: 0.554
#>
13 <R=0.554,r=0.477,N= 8>, Top: 1( 1 )[ 1 : 1 Fa= 18 : 0.477 ]( 1 , 1 , 18 ),<|>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.454
#>
14 <R=0.454,r=0.427,N= 11>, Top: 5( 1 )[ 1 : 5 Fa= 20 : 0.427 ]( 5 , 6 , 18 ),<|>Tot Used: 32 , Added: 6 , Zero Std: 0 , Max Cor: 0.579
#>
15 <R=0.579,r=0.540,N= 11>, Top: 1( 1 )[ 1 : 1 Fa= 20 : 0.540 ]( 1 , 1 , 20 ),<|>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.422
#>
16 <R=0.422,r=0.400,N= 6>, Top: 3( 1 )[ 1 : 3 Fa= 21 : 0.400 ]( 3 , 3 , 20 ),<|>Tot Used: 32 , Added: 3 , Zero Std: 0 , Max Cor: 0.478
#>
17 <R=0.478,r=0.400,N= 6>, Top: 1( 1 )[ 1 : 1 Fa= 21 : 0.400 ]( 1 , 1 , 21 ),<|>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.390
#>
18 <R=0.390,r=0.400,N= 0>
#>
[ 18 ], 0.3902384 Decor Dimension: 32 Nused: 32 . Cor to Base: 21 , ABase: 4 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
515156
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
4916
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
1.39
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
1.37
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.3902384
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47 | 2.03 | 2.64 | 1.86 | 1.11e-03 | 0.666 |
| pnodes | 4.87 | 6.02 | 2.63 | 5.21 | 6.25e-09 | 0.650 |
| worst_radius | 22.67 | 4.70 | 20.35 | 4.08 | 3.68e-01 | 0.647 |
| worst_perimeter | 151.33 | 32.42 | 135.34 | 26.85 | 5.71e-01 | 0.645 |
| mean_area | 1081.98 | 397.26 | 888.40 | 310.85 | 1.26e-01 | 0.645 |
| worst_area | 1635.77 | 703.15 | 1317.95 | 550.94 | 2.72e-01 | 0.643 |
| mean_perimeter | 121.10 | 22.91 | 110.02 | 19.19 | 4.72e-01 | 0.641 |
| mean_radius | 18.33 | 3.37 | 16.70 | 2.91 | 3.12e-01 | 0.639 |
| SE_perimeter | 4.73 | 2.21 | 3.81 | 1.80 | 6.37e-02 | 0.634 |
| SE_area | 81.97 | 53.36 | 61.22 | 37.72 | 6.46e-02 | 0.632 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47174 | 2.02985 | 2.63846 | 1.85507 | 0.00111 | 0.666 |
| La_worst_area | 228.53323 | 47.29391 | 250.81270 | 52.75337 | 0.15315 | 0.640 |
| mean_radius | 18.33087 | 3.36557 | 16.69945 | 2.91309 | 0.31204 | 0.639 |
| La_mean_perimeter | -5.45575 | 0.60054 | -5.79148 | 0.74945 | 0.05357 | 0.633 |
| La_SE_concavepoints | 0.01479 | 0.00242 | 0.01568 | 0.00324 | 0.84498 | 0.599 |
| La_worst_concavity | 0.27693 | 0.04829 | 0.25813 | 0.05620 | 0.79863 | 0.597 |
| La_SE_area | -63.19916 | 13.30716 | -61.29910 | 9.41088 | 0.98175 | 0.587 |
| La_SE_smoothness | 0.00489 | 0.00188 | 0.00550 | 0.00192 | 0.38636 | 0.573 |
| La_mean_symmetry | 0.15714 | 0.01905 | 0.16337 | 0.02694 | 0.18185 | 0.571 |
| La_SE_symmetry | -0.00325 | 0.00362 | -0.00377 | 0.00526 | 0.01253 | 0.568 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 4.54 | 28 | 0.875 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| tsize | 3.47e+00 | 2.03e+00 | 2.64e+00 | 1.86e+00 | 0.001110 | 0.666 | 0.666 | 1 | |
| worst_radius | NA | 2.27e+01 | 4.70e+00 | 2.03e+01 | 4.08e+00 | 0.368015 | 0.647 | 0.647 | NA |
| worst_perimeter | NA | 1.51e+02 | 3.24e+01 | 1.35e+02 | 2.68e+01 | 0.570824 | 0.645 | 0.645 | NA |
| mean_area | NA | 1.08e+03 | 3.97e+02 | 8.88e+02 | 3.11e+02 | 0.125673 | 0.645 | 0.645 | NA |
| worst_area | NA | 1.64e+03 | 7.03e+02 | 1.32e+03 | 5.51e+02 | 0.271758 | 0.643 | 0.643 | NA |
| mean_perimeter | NA | 1.21e+02 | 2.29e+01 | 1.10e+02 | 1.92e+01 | 0.471679 | 0.641 | 0.641 | NA |
| La_worst_area | + 178.320mean_radius -1.675mean_area + 42.622SE_perimeter -2.435SE_area -101.151worst_radius -3.788worst_perimeter + 1.000*worst_area | 2.29e+02 | 4.73e+01 | 2.51e+02 | 5.28e+01 | 0.153154 | 0.640 | 0.643 | -4 |
| mean_radius | 1.83e+01 | 3.37e+00 | 1.67e+01 | 2.91e+00 | 0.312037 | 0.639 | 0.639 | 10 | |
| SE_perimeter | NA | 4.73e+00 | 2.21e+00 | 3.81e+00 | 1.80e+00 | 0.063724 | 0.634 | 0.634 | NA |
| La_mean_perimeter | -6.657mean_radius + 1.000mean_perimeter -33.734mean_compactness + 0.581worst_radius -0.085*worst_perimeter | -5.46e+00 | 6.01e-01 | -5.79e+00 | 7.49e-01 | 0.053570 | 0.633 | 0.641 | -3 |
| SE_area | NA | 8.20e+01 | 5.34e+01 | 6.12e+01 | 3.77e+01 | 0.064638 | 0.632 | 0.632 | NA |
| La_SE_concavepoints | + 0.000mean_radius -0.001SE_perimeter -0.278SE_compactness + 1.000SE_concavepoints + 0.022*worst_compactness | 1.48e-02 | 2.42e-03 | 1.57e-02 | 3.24e-03 | 0.844985 | 0.599 | 0.466 | -1 |
| La_worst_concavity | + 0.010mean_radius + 1.570mean_compactness -1.480mean_concavity + 2.829SE_compactness -3.192SE_concavity -0.830worst_compactness + 1.000*worst_concavity | 2.77e-01 | 4.83e-02 | 2.58e-01 | 5.62e-02 | 0.798630 | 0.597 | 0.492 | -3 |
| worst_symmetry | NA | 3.14e-01 | 6.15e-02 | 3.39e-01 | 8.23e-02 | 0.069522 | 0.596 | 0.596 | NA |
| La_SE_area | + 3.076mean_radius -17.504SE_perimeter + 1.000SE_area -14.926worst_radius + 1.452*worst_perimeter | -6.32e+01 | 1.33e+01 | -6.13e+01 | 9.41e+00 | 0.981753 | 0.587 | 0.632 | -3 |
| mean_symmetry | NA | 1.88e-01 | 2.11e-02 | 1.97e-01 | 3.12e-02 | 0.151020 | 0.580 | 0.580 | NA |
| La_SE_smoothness | + 1.000SE_smoothness -0.052SE_compactness -0.103SE_concavity + 0.011worst_compactness | 4.89e-03 | 1.88e-03 | 5.50e-03 | 1.92e-03 | 0.386356 | 0.573 | 0.536 | -1 |
| La_mean_symmetry | + 1.000mean_symmetry -0.086worst_compactness | 1.57e-01 | 1.91e-02 | 1.63e-01 | 2.69e-02 | 0.181846 | 0.571 | 0.580 | 1 |
| La_SE_symmetry | + 0.044mean_symmetry -0.404SE_compactness + 1.000SE_symmetry + 0.048worst_compactness -0.115*worst_symmetry | -3.25e-03 | 3.62e-03 | -3.77e-03 | 5.26e-03 | 0.012531 | 0.568 | 0.504 | -3 |
| mean_concavity | NA | 1.62e-01 | 6.21e-02 | 1.51e-01 | 6.33e-02 | 0.499349 | 0.547 | 0.547 | NA |
| worst_compactness | NA | 3.58e-01 | 1.31e-01 | 3.87e-01 | 1.79e-01 | 0.113263 | 0.538 | 0.538 | NA |
| SE_smoothness | NA | 6.48e-03 | 2.00e-03 | 6.86e-03 | 3.30e-03 | 0.022967 | 0.536 | 0.536 | NA |
| SE_compactness | NA | 3.08e-02 | 1.75e-02 | 3.13e-02 | 1.88e-02 | 0.021169 | 0.513 | 0.513 | NA |
| mean_compactness | NA | 1.42e-01 | 4.07e-02 | 1.46e-01 | 5.30e-02 | 0.340243 | 0.505 | 0.505 | NA |
| SE_symmetry | NA | 1.99e-02 | 9.17e-03 | 2.06e-02 | 1.08e-02 | 0.000118 | 0.504 | 0.504 | NA |
| worst_concavity | NA | 4.40e-01 | 1.48e-01 | 4.42e-01 | 1.70e-01 | 0.238928 | 0.492 | 0.492 | NA |
| SE_concavity | NA | 3.83e-02 | 1.61e-02 | 3.90e-02 | 2.12e-02 | 0.046100 | 0.478 | 0.478 | NA |
| SE_concavepoints | NA | 1.44e-02 | 3.94e-03 | 1.45e-02 | 5.40e-03 | 0.107204 | 0.466 | 0.466 | NA |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 68 | 23 |
| 1 | 9 | 37 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.438 | 0.3534 | 0.525 |
| tp | 0.336 | 0.2574 | 0.421 |
| se | 0.804 | 0.6609 | 0.906 |
| sp | 0.747 | 0.6453 | 0.833 |
| diag.ac | 0.766 | 0.6866 | 0.834 |
| diag.or | 12.155 | 5.1002 | 28.966 |
| nndx | 1.813 | 1.3532 | 3.267 |
| youden | 0.552 | 0.3061 | 0.739 |
| pv.pos | 0.617 | 0.4821 | 0.739 |
| pv.neg | 0.883 | 0.7897 | 0.945 |
| lr.pos | 3.182 | 2.1743 | 4.658 |
| lr.neg | 0.262 | 0.1440 | 0.476 |
| p.rout | 0.562 | 0.4747 | 0.647 |
| p.rin | 0.438 | 0.3534 | 0.525 |
| p.tpdn | 0.253 | 0.1675 | 0.355 |
| p.tndp | 0.196 | 0.0936 | 0.339 |
| p.dntp | 0.383 | 0.2607 | 0.518 |
| p.dptn | 0.117 | 0.0549 | 0.210 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 37 | 23 | 60 |
| Test - | 9 | 68 | 77 |
| Total | 46 | 91 | 137 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.804 | 0.661 | 0.906 |
| 4 | sp | 0.747 | 0.645 | 0.833 |
| 6 | diag.or | 12.155 | 5.100 | 28.966 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 85 | 6 |
| 1 | 24 | 22 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.2044 | 0.1403 | 0.282 |
| tp | 0.3358 | 0.2574 | 0.421 |
| se | 0.4783 | 0.3289 | 0.631 |
| sp | 0.9341 | 0.8620 | 0.975 |
| diag.ac | 0.7810 | 0.7024 | 0.847 |
| diag.or | 12.9861 | 4.7298 | 35.655 |
| nndx | 2.4253 | 1.6503 | 5.239 |
| youden | 0.4123 | 0.1909 | 0.606 |
| pv.pos | 0.7857 | 0.5905 | 0.917 |
| pv.neg | 0.7798 | 0.6903 | 0.854 |
| lr.pos | 7.2536 | 3.1625 | 16.637 |
| lr.neg | 0.5586 | 0.4213 | 0.741 |
| p.rout | 0.7956 | 0.7183 | 0.860 |
| p.rin | 0.2044 | 0.1403 | 0.282 |
| p.tpdn | 0.0659 | 0.0246 | 0.138 |
| p.tndp | 0.5217 | 0.3695 | 0.671 |
| p.dntp | 0.2143 | 0.0830 | 0.410 |
| p.dptn | 0.2202 | 0.1465 | 0.310 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 22 | 6 | 28 |
| Test - | 24 | 85 | 109 |
| Total | 46 | 91 | 137 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.781 | 0.702 | 0.847 |
| 3 | se | 0.478 | 0.329 | 0.631 |
| 4 | sp | 0.934 | 0.862 | 0.975 |
| 6 | diag.or | 12.986 | 4.730 | 35.655 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 86 | 5 |
| 1 | 27 | 19 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.1752 | 0.1156 | 0.249 |
| tp | 0.3358 | 0.2574 | 0.421 |
| se | 0.4130 | 0.2700 | 0.568 |
| sp | 0.9451 | 0.8764 | 0.982 |
| diag.ac | 0.7664 | 0.6866 | 0.834 |
| diag.or | 12.1037 | 4.1275 | 35.493 |
| nndx | 2.7925 | 1.8193 | 6.831 |
| youden | 0.3581 | 0.1464 | 0.550 |
| pv.pos | 0.7917 | 0.5785 | 0.929 |
| pv.neg | 0.7611 | 0.6717 | 0.836 |
| lr.pos | 7.5174 | 2.9985 | 18.846 |
| lr.neg | 0.6211 | 0.4849 | 0.795 |
| p.rout | 0.8248 | 0.7506 | 0.884 |
| p.rin | 0.1752 | 0.1156 | 0.249 |
| p.tpdn | 0.0549 | 0.0181 | 0.124 |
| p.tndp | 0.5870 | 0.4323 | 0.730 |
| p.dntp | 0.2083 | 0.0713 | 0.422 |
| p.dptn | 0.2389 | 0.1637 | 0.328 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 19 | 5 | 24 |
| Test - | 27 | 86 | 113 |
| Total | 46 | 91 | 137 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.413 | 0.270 | 0.568 |
| 4 | sp | 0.945 | 0.876 | 0.982 |
| 6 | diag.or | 12.104 | 4.128 | 35.493 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 90 | 1 |
| 1 | 38 | 8 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.0657 | 0.030477 | 0.1210 |
| tp | 0.3358 | 0.257400 | 0.4214 |
| se | 0.1739 | 0.078203 | 0.3142 |
| sp | 0.9890 | 0.940289 | 0.9997 |
| diag.ac | 0.7153 | 0.631995 | 0.7891 |
| diag.or | 18.9474 | 2.289909 | 156.7760 |
| nndx | 6.1378 | 3.185601 | 54.0757 |
| youden | 0.1629 | 0.018493 | 0.3139 |
| pv.pos | 0.8889 | 0.517503 | 0.9972 |
| pv.neg | 0.7031 | 0.615994 | 0.7806 |
| lr.pos | 15.8261 | 2.040645 | 122.7382 |
| lr.neg | 0.8353 | 0.730259 | 0.9554 |
| p.rout | 0.9343 | 0.878957 | 0.9695 |
| p.rin | 0.0657 | 0.030477 | 0.1210 |
| p.tpdn | 0.0110 | 0.000278 | 0.0597 |
| p.tndp | 0.8261 | 0.685809 | 0.9218 |
| p.dntp | 0.1111 | 0.002809 | 0.4825 |
| p.dptn | 0.2969 | 0.219402 | 0.3840 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 8 | 1 | 9 |
| Test - | 38 | 90 | 128 |
| Total | 46 | 91 | 137 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.715 | 0.6320 | 0.789 |
| 3 | se | 0.174 | 0.0782 | 0.314 |
| 4 | sp | 0.989 | 0.9403 | 1.000 |
| 6 | diag.or | 18.947 | 2.2899 | 156.776 |
par(op)